| """Medium task — chronic disease follow-up.""" |
|
|
| from __future__ import annotations |
| from typing import Any |
| from environment.models import SOAPNote |
|
|
| MEDIUM_TASK: dict[str, Any] = { |
| "task_id": "medium_chronic_disease_followup", |
| "description": "Generate a SOAP note for a Type 2 Diabetes follow-up visit.", |
| "transcript_file": "data/transcripts/medium.txt", |
| "patient_context": { |
| "patient_id": "P-2045", "name": "Robert Smith", "age": 58, "sex": "M", |
| "known_conditions": ["Type 2 Diabetes Mellitus", "Hypertension"], |
| "current_medications": ["Metformin 1000 mg BID", "Lisinopril 20 mg daily", "Atorvastatin 40 mg daily"], |
| "allergies": [], |
| "recent_labs": {"HbA1c": "7.8%", "fasting_glucose": "156 mg/dL", "creatinine": "1.1 mg/dL", "eGFR": "78 mL/min", "LDL": "102 mg/dL"}, |
| }, |
| "max_steps": 8, |
| } |
|
|
|
|
| def grade_medium(soap_note: SOAPNote, task: dict[str, Any]) -> dict[str, float]: |
| s, o, a, p = (soap_note.subjective.lower(), soap_note.objective.lower(), |
| soap_note.assessment.lower(), soap_note.plan.lower()) |
|
|
| s_score = 0.5 * any(k in s for k in ("restaurant", "diet", "eating")) \ |
| + 0.5 * any(k in s for k in ("statin", "gap", "missed")) |
| o_score = 0.5 * any(k in o for k in ("7.8", "7.2", "a1c", "hba1c")) \ |
| + 0.5 * any(k in o for k in ("156", "fasting glucose", "glucose")) |
| a_score = 0.5 * any(k in a for k in ("diabetes", "t2dm", "dm")) \ |
| + 0.5 * any(k in a for k in ("hypertension", "htn", "blood pressure")) |
| p_score = 0.5 * ("glipizide" in p and any(k in p for k in ("5", "add"))) \ |
| + 0.5 * ("lisinopril" in p and any(k in p for k in ("40", "increase", "uptitrat"))) |
|
|
| clamp = lambda v: max(0.01, min(v, 0.99)) |
| return { |
| "subjective_accuracy": clamp(s_score), |
| "objective_accuracy": clamp(o_score), |
| "assessment_accuracy": clamp(a_score), |
| "plan_accuracy": clamp(p_score), |
| } |
|
|
